Spatial mixing and approximation algorithms for graphs with bounded connective constant

TL;DR

This paper establishes a connection between the connective constant of a graph and strong spatial mixing in the hard core model, leading to efficient approximation algorithms for the partition function on various graph classes.

Contribution

It introduces a novel relationship between connective constant and spatial mixing, enabling FPTAS for the hard core model on graphs with unbounded degrees.

Findings

Strong spatial mixing occurs below a critical activity lambda_c(Delta+1) for graphs with connective constant Delta.

An FPTAS for the partition function is achievable on graphs from G(n,d/n) for lambda < e/d.

Improved bounds for spatial mixing on lattices in higher dimensions.

Abstract

The hard core model in statistical physics is a probability distribution on independent sets in a graph in which the weight of any independent set I is proportional to lambda^(|I|), where lambda > 0 is the vertex activity. We show that there is an intimate connection between the connective constant of a graph and the phenomenon of strong spatial mixing (decay of correlations) for the hard core model; specifically, we prove that the hard core model with vertex activity lambda < lambda_c(Delta + 1) exhibits strong spatial mixing on any graph of connective constant Delta, irrespective of its maximum degree, and hence derive an FPTAS for the partition function of the hard core model on such graphs. Here lambda_c(d) := d^d/(d-1)^(d+1) is the critical activity for the uniqueness of the Gibbs measure of the hard core model on the infinite d-ary tree. As an application, we show that the partition function can be efficiently approximated with high probability on graphs drawn from the random graph model G(n,d/n) for all lambda < e/d, even though the maximum degree of such graphs is unbounded with high probability. We also improve upon Weitz's bounds for strong spatial mixing on bounded degree graphs (Weitz, 2006) by providing a computationally simple method which uses known estimates of the connective constant of a lattice to obtain bounds on the vertex activities lambda for which the hard core model on the lattice exhibits strong spatial mixing. Using this framework, we improve upon these bounds for several lattices including the Cartesian lattice in dimensions 3 and higher. Our techniques also allow us to relate the threshold for the uniqueness of the Gibbs measure on a general tree to its branching factor (Lyons, 1989).